Feature Extraction of Tea Leaf Images using Dual-Tree Complex Wavelet Transform and Gray Level Co-occurrence Matrix

Content Provider	Scilit
Author	Alma Iswanto, B. H. Sugihartono, I.
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series Feature extraction is a very important part of machine learning to analyze and find relationships between objects of different categories. This paper aims to analyze the feature vectors of the tea leaf image produced by using a combination of dual-tree complex wavelet transform and gray level co-occurrence matrix techniques. The tea leaf image consists of four different categories, each representing different phases of tea growth and were acquired using a visible camera from eight different orientations. Feature extraction using Principle Component Analysis (PCA) shows that the texture features can identify a different category of the leaf images without being significantly affected by the difference in scale and orientation of the images.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/2019/1/012092/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/2019/1/012092
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	2019
Language	English
Publisher	IOP Publishing
Publisher Date	2021-10-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Leaf Image Feature Extraction
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy